Seattle-Portland Air Pollution Study

Environment and Planning B: Planning and Design 2007, volume 34, pages 154-170

DOI:10.1068/b32124

The exposure of disadvantaged populations in freeway
air-pollution sheds: a case study of the Seattle and Portland
regions

Chang-Hee Christine Bae, Gail Sandlin, Alon Bassok

Department of Urban Design and Planning, College of Architecture and Urban Planning,
University of Washington, Seattle, WA 98195-5740, United States;

e-mail: cbae@u. washington.edu, gsandlin@u. washington.edu, abassok@u. washington.edu
Sungyop Kim

Department of Architecture, Urban Planing and Design, University of Missouri-Kansas, 213 Epperson,
511 Rockhill Road, Kansas City, MO 64110-2499, United States; e-mail: kims@umkc.edu
Received 7 August 2005; in revised form 29 January 2006

Abstract. Freeway-related air pollution and its harmful health risks have been observed in recent
research in the environmental-health sciences. In this study we investigate the impact of freeway and
arterial-road air pollution on vulnerable populations — for example, the poor, minorities, children,
and the elderly — whose housing options are limited. Because many mobile-source emissions decay
rapidly with distance, approaching background concentrations at 330 ft from the freeway, populations
living near limited access roads are most at risk from exposure. Furthermore, microscale air monitor-
ing systems are rarely in place at these locations in the United States. In this research we will define
freeway air-pollution sheds with the aid of a geographic information system analysis and determine
populations that may be at risk from exposure to mobile-source pollutants in two West Coast metro-
politan areas (Seattle and Portland). We then use cluster analysis to identify key neighborhoods at risk
in Seattle. Subsequently, we apply a hedonic pricing model to understand the extent to which house
price values in Seattle are related to freeway proximity. Finally, we discuss policy options, planning
implications, and mitigation measures, including an assessment of air-quality monitoring needs and
land-use prescriptions.

1 Introduction

Traditionally, environmental-justice studies have focused on the disparate impact of air
pollution on low-income and/or minority populations residing near stationary or point
sources from industrial facilities or locally undesirable land uses. (1) However, if road-
ways are viewed as pollution line sources then an examination of populations living
near high traffic densities that release both criteria as well as hazardous air pollutants,
also known as 'air toxics') may also have environmental-justice implications. Six criteria
pollutants are listed under the National Ambient Air Quality Standards required by the

(1) A recent example is the results of a lawsuit filed by the Air Quality Management District
(AQMD) in Southern California against the oil company BP/Arco in March 2005. The lawsuit
sought $319 million because of violations at the company's Carson refinery between 1994 and 2002,
the claim being based on the maximum penalty for each of thousands of violations. Within a week,
BP/Arco settled for a total of $81 million ($6 million in past fees, $25 million in cash penalties,
$30 million in community programs for asthma diagnosis and treatment, and $20 million in
emissions-reduction measures). This was by far the largest settlement ever negotiated by the
AQMD, the country's largest and most effective air-pollution agency. Although the Carson refinery
is a stationary source and not located next to a freeway, it is adjacent to a major arterial
(Sepulveda Boulevard). Moreover, although Carson is one of the most racially integrated cities in
the United States, most of the residents near the refinery are minorities. Thus, although this paper
is focused on mobile sources, we do not want to give the impression that stationary-source
pollution is not a problem. Indeed, when stationary polluting sources are located near freeways
or arterials, nearby residents are in double jeopardy (Bacerra, 2005).

Disadvantaged populations in freeway air-pollution sheds

155

Table 1. Criteria pollutants — National Ambient Air Quality Standards. PM 10 and PM 2 5 refer to
particulate matter of less than 10 ^m and 2.5 um, respectively.

Clean Air Act —
criteria pollutants

Carbon monoxide

PM 10

PM 25

Ozone

Sulfur dioxide
Nitrogen dioxide
Lead

1 hour
average

35 ppm

na

na

0.12 ppm

na

na

na

Annual arithmetic
mean

na

50 ug nT 3
15 ug nT 3
na

0.03 ppm
0.053 ppm
na

8 hour
average

9 ppm

na

na

0.08 ppm

na

na

na

24 hour average
concentration

na

150 ug nT 3
65 ug m~ 3
na

0.14 ppm
na

1.5 ug m~ 3
not applicable.

Notes: ppm — parts per million; ug m 3 — microgram per cubic meter; na —

Clean Air Acts (see table 1). Emissions from the transportation sector (in 2002, the
latest year for which data are available) accounted for 77.3% of carbon monoxide (CO);
54.3% of nitrogen oxides (NOJ; 44.8% of volatile organic compounds (VOCs),
12% of lead (Pb) releases, 6.4% of particulate matter of less than 2.5 um (PM 2 5 ). 2.3%
of particulate matter of less than 10 um (PM 10 ), and 4.5% of sulfur oxide emissions
(SO z ) (US Environmental Protection Agency, 2005). In addition, there are hundreds
of elements and compounds emitted from the on-road and off-road vehicles. With
support of recent findings from environmental-health sciences and epidemiology, the
US Environmental Protection Agency recently identified a list of twenty-one mobile
source air toxics. This listing is "to capture the collection of emissions potentially
responsible for the cancer and noncancer health effects related to diesel exhaust"
(US Environmental Protection Agency, 2001, page 8). Although the six criteria pollu-
tion levels have been reduced under the Clean Air Acts since the 1970s (Bae, 2004),
researchers are finding more health risks related to, especially combustion-related, air
toxics and ultrafine particles. For example, the Puget Sound Clean Air Agency reports
that diesel soot is responsible for 70 - 80% of air-toxic-related cancer risks. The average
air-toxic-related cancer risks are estimated to be in the 400 - 700 per million range in
the Seattle region (Puget Sound Clean Air Agency, 2003, pages ES-4, ES-5).

Moreover, traffic is also a source of noise and vibration with the greatest impact on
those living within 500 ft of major roads (US Department of Transportation, 1997).
Although criteria pollutants and urban air toxics may also have microscale impacts,
unlike noise, which is commonly measured and mitigated, mobile-source microscale
'hot spots' — for example, the roadside, freeways, rail roads, and bus depots — are rarely
monitored in the United States because the Clean Air Act only requires ambient air-
quality monitoring. These new findings are rarely reflected in local land-use planning
decisionmaking processes (California Environmental Protection Agency and California
Air Resource Board, 2005). As the urban-planning profession pays more attention to
efforts to reduce urban sprawl via compact-city policies (for example, urban growth
boundaries, smart growth), there is the threat of human-health costs to those living near
freeways. In this paper we investigate land-use and population patterns near major roads
in the Seattle and Portland metropolitan areas. Low-income and minority populations
may be more at risk of exposure to mobile-source pollutants because of the search for
affordable housing and because of land-use and transportation planning practices.

Although the amount of pollution released from mobile sources such as trucks and
automobiles is dependent on such factors as traffic volume, fleet composition, fuel
type, control technology, and vehicle speed, the potential for population exposure to
these pollutants is also dependent on area topography, meteorological conditions, and

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C-H C Bae, G Sandlin, A Bassok, S Kim

Table 2. Adverse health effects of mobile-source pollutants [modified from table 4.3 of Bates and
Caton (2002)].

Pollutant

Definitive effect

Probable effect

Ozone

Fine particles aggravation of asthma (English

et al, 1999)
depressed lung function in school

children (Wjst et al, 1993)
increased risk of lung cancer
increased prevalence of bronchitis

increased hospital admissions for

acute respiratory diseases
aggravation of asthma
increased bronchial responsiveness
reduced lung function
increased school absences for
respiratory illness

increased response to allergens
(Janssen et al, 2003)
increased airway inflammation

increased respiratory morbidity

(Nitta et al, 1993)
aggravation of asthma in children

(Delfino et al, 2003)
reduced rate of lung growth

Carbon monoxide increased cardiac ischaemia
a VOCs — volatile organic compounds.

Diesel /VOCs a
(in addition to
particle effects)

Nitrogen dioxide

aggravation of acute respiratory
symptoms (van Vliet et al, 1997)

increased risk of wheezy bronchitis
in infants (Gehring et al, 2002)

decreased rate of lung growth in
children

effect on mortality

increased risk of lung cancer
increased risk of childhood

leukemia (Crosignani et al, 2004)

increased hospital cardiac
admissions

pollutant dispersion patterns, in addition to demographic and human-activity factors.
Despite the complexity of these variables, there is a growing body of environmental-
health and epidemiological evidence that populations living near limited-access freeways
or high-traffic-density arterials may be at increased risk of exposure to mobile-source
pollutants, which result in adverse health effects, such a lung cancer, leukemia, and
asthma (see table 2 for details). (2)

2 Literature review

Proximity to high traffic densities can be considered a surrogate for individual expo-
sure, although researchers have used a variety of methodologies to measure proximity
and traffic volume and/or densities. Huang and Batterman (2000) examined forty-five
epidemiological studies that used residential location as a measure of environmental
exposure, with distance from the pollution source to the receptor (school, residence)
as the most common methodology for determining exposure. Several studies have
used circular buffer zones around the receptor, assuming equal pollution dispersion,
and others have used a combination of receptor zones and proximity to major roads.

A Tokyo study determined that, on the basis of the dispersion gradient of NO x ,
traffic emissions could have adverse health effects on populations residing within 150 m
of major roadways (Nitta et al, 1993). The validity of distance from major roadways as
a measure of exposure to air pollution from traffic was investigated by a Dutch team

(2) Workers at sites close to freeways and major roads are also at risk, but these are not specifically
included in our study.

Disadvantaged populations in freeway air-pollution sheds

157

who measured outdoor levels of PM 10 , PM 25 , black smoke, benzene, and N0 2 at
different distances from the roadway (Roorda-Knape et al, 1998). This study concluded
that traffic intensity, distance, and wind direction are all important variables when
considering population exposure to mobile-source pollutants. Wind direction was also
important in an Australian study (Hitchins et al, 2000).

Another methodology adopted by researchers investigating an association of child-
hood asthma with proximity to roadway pollution was based on determining traffic
density within residential buffer zones (Lin et al, 2002). In this study, traffic density
was calculated by multiplying the length of road segment within the buffer by the
annual average daily traffic count of the specific roadway. Traffic density was then
categorized into low, medium, or high, with the latter specified as ^ 4043 vehicle miles
traveled (VMT) in a 200 m buffer or 18 765 VMT in a 500 m buffer. The study
concluded that there was a correlation between high volumes of traffic or a high
percentage of trucks within a 200 m residential buffer for children hospitalized with
asthmatic episodes. Zhu et al monitored ultrafine particles (that is, less than 0.1 um
in diameter) at various distances from a nine-lane freeway in Southern California
in order to determine the dispersion gradient of these particles (see figure 2 of http://
www.ph.ucla.edu/magazine/PHmgnov02research.pdf and Zhu et al, 2002 for details).
The researchers concluded that people who live, work, or travel within 100 m downwind
of major traffic sources may have a much higher ultrafine-particle exposure than those
who live further from such sources.

Of particular interest in epidemiological research is the effect of mobile-source
pollutants on sensitive populations such as children. Wjst et al (1993) reported that
with each increase of 25 000 cars on a main road near schools the lung function of
children decreased by 0.71%. This work was soon followed by various research initia-
tives, especially led by Brunekreef and his colleagues in the Netherlands. Their findings
indicated that children living or attending schools within 100 m (330 ft) of truck traffic
had poorer lung function, leaving the researchers to hypothesize that this may be
the result of long-term exposure to ultrafine particles (Brunekreef et al, 1997). The
Brunekreef research team also conducted a detailed study of respiratory symptoms.
After accounting for confounding factors of socioeconomic status and related life-
style considerations (for example, smoking in the home, unvented gas-fired heaters)
their results suggested that an association between traffic-related air pollution
and respiratory health was mainly restricted to the children of intermediate and
low socioeconomic status (van Vliet, 1997). Such findings imply that a demographic
analysis of populations living near major arterials could reveal that minority or
low-income populations are disproportionately impacted by mobile pollution sources.

Several US-based studies were also concerned with the environmental-justice impli-
cations of exposure to mobile-source pollutants; that is, are low-income or minority
populations more at risk? (Delfino et al 2003; English et al, 1999; Forkenbrock and
Schweitzer, 1999; Green et al, 2004; Gunier et al, 2003; Kinney et al, 2000; Korenstein
and Piazza, 2002; Lena et al, 2002; Loh and Sugerman-Brozan, 2002; Samet et al, 2001 ;
Wilhelm and Ritz, 2003.) The Forkenbrock and Schweitzer study adopted a similar
procedure to the one used in this research, a geographic information systems (GIS)
approach at the census-block level, and developed a pollutant-dispersion model to
measure the impact of highway projects on low-income and/or minority populations
in the City of Waterloo, Iowa. However, their research is more of a demonstration of an
air-pollution and noise-pollution modeling methodology to a very small microlocation
than a metropolitan-level analysis. A more traditional epidemiological approach was
conducted by English et al (1999), also using GIS, to determine whether living near
busy roads may be associated with asthma among children of low-income populations

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C-H C Bae, G Sandlin, A Bassok, S Kim

in San Diego County, California. This study concluded, on the basis of traffic counts
within a 550 ft (~ 165 m) buffer of children's homes, that living near high-volume traffic
was a contributing rather than a causal factor in asthma development. The buffer size was
selected on the basis of an examination of several air-emission dispersion models that
indicated a 80-90% decay of pollutants between 492 ft and 656 ft. It is also important to
note that the earlier Dutch study by Roorda-Knape et al (1998) suggests that traffic
pollutants in or near schools are a more relevant measure of exposure because children
spend most of the daytime at school during periods of high traffic flow.

The California-based research of Korenstein and Piazza (2002), Green et al (2004),
and Gunier et al (2003) further investigated the proximity of schools to major roads.
Korenstein's team developed dispersion-modeling estimates of PM 10 concentrations at
four predominantly Hispanic urban schools, three of which were 150 m (~ 500 ft) from
major roads with up to 250000 vehicles per day (VPD). Although the results indicated
that predictive concentrations were much lower than regulatory levels (either federal or
California standards), the authors stated that below-regulation concentrations have
still been shown to cause significant negative health effects.

Green et al (2004) studied the demographics of California schools (173 schools with
more than 105 000 students) located within a 150 m (~ 500 ft) high traffic buffer and
found that, as the traffic-exposure category increased, then the percentages of Hispanic
and of non-Hispanic black children attending schools in those categories increased and
the percentage of non-Hispanic white children decreased. The study also found that
poverty was related to traffic exposure, which supports the earlier findings of Gunier
et al, in which the researchers concluded that Hispanic, African-American, and Asian
children in the lowest income quartile were on average three to five times more likely
than children in the highest income quartile to live in block groups with high traffic
densities (Gunier et al, 2003). Community-based studies of the Bronx, New York
(Kinney et al, 2000), and of Roxbury, Massachusetts (Loh and Sugerman-Brozan,
2002), found similar results. A survey of 1109 parents in East Bay, San Francisco, that
inquired about their children, found a strong association between asthma symptoms and
NO, and PM levels (Kim et al, 2004).

Currently in the United States there are few policy initiatives beyond the regulatory
requirements of the Clean Air Act to address the adverse health impacts from exposure
to mobile sources. In California, Senate Bill 352 prohibits the siting of new schools
within 500 ft of a busy road, defined as traffic in excess of 50 000 VPD in a rural area
and 100 000 VPD in an urban area (California Department of Education, 2004).

Two recent court cases also had the potential to influence public policy. The first
involved a lawsuit filed by the Sierra Club and other litigants regarding the need for a
supplemental environmental impact statement to address the potential impacts of
a highway expansion in Las Vegas, Nevada, on the health of nearby residents. Consid-
ered a potentially precedent-setting case, the US District Court for the District of
Nevada recently found that the Federal Highway Administration met its requirements
in issuing an environmental-impact statement and denied all seven counts of the Sierra
Club's summary judgment motion [Sierra Club v Mineta D.Nev., (No. CV-S-02-0578-
PMP-RJJ)]. In 2002 several environmental and labor groups filed suit against the US
Department of Transportation with respect to the need for considering the localized
adverse impacts of Mexican truck traffic. In 2003 the US 9th Circuit Court of Appeals
ruled that the National Environmental Protection Act required the US Department of
Transportation "to consider the most likely localities to be affected by increased truck
traffic and to perform more localized analyses for these areas" and that simply placing
the potential pollution increases in the context of US national emissions was inade-
quate [Public Citizen v Dept of Transportation (No. 02-70986 9d Cir. 2003)]. This ruling

Disadvantaged populations in freeway air-pollution sheds

159

suggests that regional air-quality monitoring does not provide adequate data sources
for microscale environments, such as populations living near major roads.

3 Data and GIS analysis

In our research we examine land-use patterns near major roads to determine the
number of residences impacted and to identify the populations at risk of exposure to
mobile-source air pollution. We also examine the demographics of populations that live
near limited-access freeways in the Seattle and Portland metropolitan areas, in order to
determine if low-income, minority populations are at increased risk of exposure
to mobile sources. In addition, we investigate the prevalence of schools and senior
facilities located within the areas that we describe as a freeway air-pollution shed
(FAPS), specifically a 100 m (330 ft) buffer from roadways with a minimum of 100 000
VPD, a definition that encapsulates the results of the research investigations. The 330 ft
buffer is supported by several research studies, both in the United States and in other
countries (especially Zhu et al, 2002 and Brunekreef et al, 1997), although, as pointed
out above, California's statutory limit (as an example) is 500 ft. The rationale for the
buffer of this extent is the distance decay rate for ultrafme particles from freeways
(and arterial roads). Our study is an attempt to bridge the inderdisciplinary knowledge
required for land-use, transportation, and air-quality planning to identify whether there
is a disproportionate impact on minority and low-income populations.

Our study areas include the urban-growth boundary regions of Seattle, WA, and
Portland, OR. In Washington State this area was defined as approximately 310 000 acres
of western King County. The Portland study area was defined by the three Oregon
Counties of Clackamas, Washington, and Multnomah, totaling approximately 232 000
acres.

From the Census 2000 Summary File 3, we compiled demographic, social, eco-
nomic, and housing data at the census-block group level, the smallest geographic unit
for the required socioeconomic data, for these areas. Parcel data were combined
with tax-assessor records to develop a database of single and multifamily residences.
The multifamily-residence data include the number of dwelling units in each building,
which was used to assess the number of housing units in each block group.

An exhaustive list of schools (K- 12) was collected from both King County and the
Portland metropolitan area. The locational information was combined with specific
data from each respective State's Department of Education database to show enroll-
ment, demographics, and the number of students on free or reduced lunch programs.

We obtained average annual daily traffic (AADT) volume data for 2000 for all
limited-access arterials in western King, Clackamas, Multnomah, and Washington
counties. High-traffic roads were identified as having more than 100000 vehicles per
day. Specifically, these were Interstates 5, 405, and 90; state road 520 in western King
County; and Interstates 5, 84, 205, and 405 in the Portland urban growth area (UGA).

Schools, parcels, census-black groups, and traffic files were compiled within a GIS
(ArcView). Parallel lines were then created within 400 ft of the selected road network. This
buffer accounted for both the 330 ft conservative dispersion estimate of mobile-source
pollutants as well as an additional area of the roadway (ArcView measures distance
from the middle of the roadways and not their edge). These areas, designated as FAPS,
were analyzed for socioeconomic demographics, single versus multifamily residences, and
schools.

Although the census-block group is larger than the FAPS, we had little alternative
than to ascribe to socioeconomic characteristics of the block group population to its
FAPS subset. A proportion was created by calculating the number of residential units

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C-H C Bae, G Sandlin, A Bassok, S Kim

in the FAPS and those in the block group, and by using this as a ratio to estimate the
demographic characteristics of the FAPS.

4 Descriptive results

An interesting comparison emerges when we look at the Seattle and Portland FAPS.
Although the Portland UGA is smaller than the King County's UGA discussed in this
paper, in terms both of population and of area, it is slightly denser (5.7 and 4.8 persons
per acre, respectively). Despite the higher density, the proportion of persons living
within Portland's FAPS is considerably smaller than that living in King County's,
0.42% versus 1.81%. A striking finding is that single-family home development since
1990 was five times higher than in the 1980s in the Seattle FAPS. In both areas,
poor and/or African-American residents are represented in disproportionately higher
numbers in the FAPS. The number of poor living in the Seattle and Portland FAPS
is more than 1.21 and 1.36 times higher, respectively, than that in the UGAs at large,
and the concentration of African-Americans is higher than that of other minority
groups (Asian-Americans and Hispanics), and 2-3 times higher than that of the general
UGA population. In summary, there is a strong tendency for low-income and /or
nonwhite populations to live in FAPS (see tables 3 and 4).

Although the concentration of children in the core areas of Seattle and Portland is
not particularly high, it is worthwhile to look at the number of schools and students in
both FAPS. This illuminates the potential harm to young populations. Of the sixteen
educational facilities within the Portland and Seattle FAPS, there are ten elementary
schools, two middle schools, three high schools and an administration building. Total
student enrollment is more than 6600. All these schools have a high proportion of
minority students and students from low-income household (as reflected in the receipt
of free or subsidized meal programs), except for the two private schools in suburban
Bellevue/ 3 ' Seven of the FAPS schools have a nonwhite majority (between 52% and
95%), and more than 50% of students in five schools received free or subsidized meals.

5 Cluster analysis

Focusing on Seattle as our first and local study area, we applied a cluster analysis to
assign all the 2000 Census-block groups in the Puget Sound region (2750 of them) to see
whether there is a spatial residential association with income level and race. Though
each block group has its own characteristics, it is useful to classify the census-block
groups into several clusters that have similar characteristics; these may or may not be
spatially contiguous.

Cluster analysis partitions data into meaningful subgroups. It is particularly useful
when the number of subgroups and other composite information are unknown a priori
(Fraley and Raftery, 1998). The result of cluster analysis is a number of heterogeneous
groups with a more or less homogenous content. A major task is identifying the
optimum number of clusters. There is no generally accepted theory, so decisions are
based on subjective interpretation. Fraley and Raftery (1998; 1999; 2002) developed a
model-based clustering analysis technique using an expectation maximization (EM)
algorithm. Traditional cluster analysis often has problems in determining the structure
of clustered data. However, the EM approach significantly alleviates these problems.

<3) Whereas FAPS schools in low-income communities tend to have minimal or no buffers next to
the freeway, these two private schools are surrounded by dense trees. This hints that there may be
some awareness among educators and parents in wealthy communities about the problems asso-
ciated with proximity to freeways. On the other hand, it could be a signal of less-than-perfect
information even among the wealthy and highly educated, because trees are a much better noise
buffer than a pollution filter.

Disadvantaged populations in freeway air-pollution sheds

161

Table 3. Socioeconomic characteristics of Seattle's urban growth area (UGA) and freeway
air-pollution sheds (FAPS). Source: US Census of Population (2000).

Variables UGA Percentage FAPS Percentage Percentage FAPS /UGA

of UGA share ratio

Area (acres)

310 380

17073

5.50

Population

1 491 633

100

26977

100

1.81

White

1060 551

71.10

17813

66.03

1.68

0.93

Black

87 241

5.85

2 947

10.92

3.38

1.87

Hispanic

86 956

5.83

1483

5.50

1.71

0.94

Asian

176 363

11.82

3 462

12.83

1.96

1.09

Children

283 847

19.03

3 864

14.32

1.36

0.75

Seniors

165 144

11.07

2 789

10.34

1.69

0.93

Income below

132950

8.91

2912

10.79

2.19

1.21

poverty

Schools a

427

8

1.87

11 Source: Washington State Department of Education.

Table 4. Socioeconomic characteristics of Portland's urban growth boundary (UGB) and freeway
air-pollution sheds (FAPS). Source: US Census of Population (2000).

Variables UGB Percentage FAPS Percentage Percentage FAPS /UGB

of UGB share ratio

Area (acres)

232 380

6415

2.76

Population

1 328 195

100

5 633

100

0.42

White

1 047 027

79

3 891

69

0.37

0.88

Black

40 996

3

532

9

1.30

3.06

Hispanic

110492

8

469

8

0.42

1.00

Asian

74452

6

389

7

0.52

1.23

Children

289 096

22

1076

19

0.37

0.88

Seniors

138519

10

630

11

0.45

1.07

Income below

128319

10

741

13

0.58

1.36

poverty

Schools a

366

8

2.19

a Source: Oregon State Department of Education (2004).

It assumes that the data are generated by a mix of underlying probability distributions
in which each component represents a different cluster or group. The EM clustering
algorithm computes probabilities of cluster memberships on the basis of one or more
probability distributions instead of assigning cases or observations to clusters to
maximize the differences. The clustering algorithm then chooses the number of
clusters that maximize the overall probability or likelihood of the data using the
Bayesian information criterion.

In our study, this approach yielded seven clusters based on income and race. As
suggested by the data in table 5, clusters 1 and 2 are significantly different from the others,
especially in terms of racial mix and income. Cluster 1 is characterized by the only
minority-group majority share (75%) and highest percentage of children and the elderly
(33%). The general location of this cluster is south of downtown Seattle and west of
Lake Washington and the Port of Tacoma. The next cluster (cluster 2) has a much
lower income ($34634) level than that of clusters 3-7 (which range approximately
from $50000 to $90 000) although its white-population share is not much lower than
that of cluster 3. Another group near the freeway is characterized by high income

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Table 5. Cluster analysis of 2000 Census-block groups, Central Puget Sound, WA. Source: US
Census of Population (2000).

Cluster Percentage white Median household Median housing Percentage children
population income ($) value ($) and the elderly of

total population a

1 35 38 696 161445 33

2 69 34 634 151 566 28

3 88 53012 155957 31

4 73 57986 191 837 30

5 93 64458 241 644 30

6 87 71 355 339953 27

7 81 93 342 563 392 32

a Children defined as being less than 18 years of age, the elderly defined as those over 65 years
of age.

and is white dominant (cluster 6). The contrast with clusters 1 and 2 is striking, but
the explanation is proximity to Lake Washington and mountain views, Microsoft,
and somewhat lower traffic volumes. Figure 1 shows the spatial distribution of
clusters in the Puget Sound region, and confirms our a priori expectation of low-
income and minority households living close to freeways (especially the Interstate 5
corridor) and major arterials.

6 Hedonic price analysis

If there is residential market segmentation, how do residents value living near free-
ways? Do residents capitalize the negative externalities into lower house prices? To test
this, we undertook a hedonic price analysis of house-price sales data (in 2000) in King
County, WA, the core county of the Seattle metropolitan region. King County property
sales records, parcel data, census-block-group-level information, and regional travel
and employment data from the Puget Sound Regional Council (the regional Metropol-
itan Planning Organization) were used. Only single-family houses were included in the
analysis.

Housing sales transactions may not occur at random among the stock of housing
units. Housing units that were sold in year 2000 may differ in measured and unmeas-
ured characteristics from the houses not sold. However, we analyzed only the houses
sold. Therefore, there is a potential selection bias problem. The unmeasured character-
istics may lead to biased estimates of the parameters; to avoid this, we used a popular
selection bias detection and correction method [the Heckman two-step estimation
procedure (Heckman, 1979)].

The Heckman procedure consists of two stages. In the first stage a probit model
is estimated to predict the houses sold in year 2000 with the data that contain all
houses in the study area. The residuals of the probit model reveal information about
the unmeasured characteristics that distinguish sold housing from unsold housing.
The inverse Mill's ratio of selection-bias control factor called lambda is calculated
in the process through the use of normally distributed residuals. In the second stage
a regression analysis is performed with the selection-bias control factor lambda as an
additional covariate. This factor reflects the effect of all the unmeasured characteristics
related to the sales transaction choice, and the coefficient captures the partial effect
of these unmeasured characteristics. The process produces unbiased estimates for the
covariates in the hedonic pricing model. Additional steps to correct the standard errors

Disadvantaged populations in freeway air-pollution sheds

163

Figure 1. The spatial distribution of residential clusters in Central Puget Sound, WA.

were also performed (for detailed information for the application of the Heckman
procedure, see Smits, 2003).

Also, in the hedonic pricing model spatial autocorrelation is an important issue
because it results in biased parameter estimates. We included census-block-group-
level neighborhood information to capture the effects of neighborhood. However,
spatial autocorrelation is largely attributed to omitted variables, and a degree of spatial
autocorrelation may be unavoidable. Using CrimeStat, a spatial-analysis software
program, we conducted a spatial-autocorrelation test. The results show that the Moran's

164

C-H C Bae, G Sandlin, A Bassok, S Kim

/ value and z-statistic value of normality for the residuals of the predicted dependent
variable (log of sales price) were 0.0067 and 21.24, respectively. The Moran's / value
indicates that there is positive and statistically significant spatial autocorrelation. However,
the Moran's / value and z-statistic value for the observed dependent variable were 0.1253
and 383.02, respectively, suggesting a significant reduction in spatial autocorrelation.

In table 6 the results of the hedonic regression model are adjusted for selection bias
and the correction of standard errors. A significant negative parameter estimate of
lambda implies that the houses sold in 2000 compared with the houses not sold have
unmeasured characteristics negatively related to sales price.

The dependent variable, housing sales price, was log-transformed because the price
does not have negative values and is not bounded, and its distribution is skewed to the
left. Only the statistically significant independent variables at the 0.05 level were
retained in the model. Table 6 also includes a variance inflation factor (VIF) to
examine multicollinearity among the independent variables. The VIF values indicate
that the independent variables in the models are not collinear.

Table 6. Results of the hedonic pricing model for King County, Seattle metropolitan area.
Source: King County Department of Assessment (2000).

Coefficient SE a Pr > |/| VIF b Elasticity

Intercept

11.2008

0.5234

< 0.0001

Housing characteristics

Lot size (x 1000 ft 2 )

0.0017

0.0001

< 0.0001

1.1165

0.17

Total living area (x 1000 ft 2 )

0.1660

0.0090

< 0.0001

3.1739

16.62

Year built

0.0006

0.0002

0.0070

2.9734

0.06

Construction quality

0.1260

0.0063

<0.0001

3.7865

12.52

Housing renovated

0.1177

0.0257

< 0.0001

1.1330

11.05

Housing condition good

0.0498

0.0112

<0.0001

1.3385

4.51

Housing condition very good

0.1512

0.0213

< 0.0001

1.2321

15.09

Parcel characteristics

View

0.0677

0.0205

0.0010

1.2632

5.91

Waterfront

0.2868

0.0428

< 0.0001

1.7782

30.40

Tideland/shoreland

0.3330

0.0663

<0.0001

1.7496

34.97

Traffic noise moderate

-0.0399

0.0186

0.0326

1.0540

-4.80

Traffic noise high

-0.0457

0.0159

0.0042

1.1557

-5.22

Other nuisances

-0.0804

0.0243

0.0010

1.0733

-8.84

Stream

0.1818

0.0414

<0.0001

1.0911

17.48

Within FAPS C

-0.0780

0.0155

< 0.0001

1.1912

-8.21

Neighborhood (census-block group)
Population density (x 100 000 ft 2 )

characteristics

0.0026

0.0004

<0.0001

1.7844

0.26

Median housing value (x$1000)

0.0013

0.0000

< 0.0001

2.1403

0.13

Average household size

-0.1044

0.0136

< 0.0001

2.6643

-10.56

Percent homeowners

0.0026

0.0003

<0.0001

2.8850

0.26

Percent commuting by automobile

-0.0038

0.0007

< 0.0001

2.3376

-0.38

Percent black population

-0.0053

0.0009

<0.0001

1.4874

-0.53

Retail service accessibility

0.0823

0.0077

<0.0001

2.6194

8.23

Lambda

-0.8205

0.0841

<0.0001

2.6194

Adjusted R 2 = 0.7271
n = 5237

a Standard error.

b Variance inflation factor.

c Freeway air-pollution shed.

Disadvantaged populations in freeway air-pollution sheds

165

The elasticity coefficient shows the proportional change in the dependent variable
associated with a proportional change in independent variable. However, in this case we
calculated the proportional change in sales price for a one unit change in the independ-
ent variable in order to permit an easy interpretation of the effects of explanatory
variables on the response variable. For instance, a 1000 ft 2 increase in total living space
results in a 16.62% change in the predicted sales price of single-family housing, holding
all other things constant. The elasticities of the binary variables were also calculated.
The elasticity of a dummy independent variable indicates the proportional change in the
dependent variable associated with a binary change in the independent variable. For
example, the key to this analysis, the sales price of single -family housing inside FAPS is,
ceteris paribus, 8.21%, 100{exp[jS — (var/?/2)] — 1}, lower than housing outside FAPS.
This is larger than the characteristic most closely associated with locations near free-
ways — that is, traffic noise (4.8-5.2% comparing moderate to high noise levels). The
inference is that, even with imperfect information, pollution is more important than noise.

7 Qualifications

There are three major qualifications to this research. First, our focus on the long-term
health impacts of air pollution (especially PM) should be tempered by the increasing
recognition that there may be health damages associated with even short-term expo-
sure to air pollution. This has been noticed by several research studies in different
locations with different air pollutants. Examples include the exposure of children to
diesel-related pollutants during school-bus commutes in Los Angeles (Winer et al,
2005), the ozone exposure of schoolchildren, especially during school breaks (Peters
et al, 1999), the exposure to fine PM of home dwellers (both indoor and outdoor) and
workers in Helsinki (Jantunen et al, 2004), and the CO exposure of street sellers in
Mexico City (Fernandez-Bremauntz et al, 1993).

The second qualification is related to the fact that there is no widespread roadside
monitoring in the United States, as opposed to many European countries, so that our
research is based on inferences drawn from ambient air-quality levels and from the
small number of sample surveys that have been undertaken. Furthermore, the officially
sponsored studies in the United Kingdom reveal ambiguous conclusions about whether
PM emissions close to roads exceed even long-term standard objectives. For example,
data for 1999 or 2000 in fifteen cities exceeded the 2004 standard at only five of the
thirty-four sites, whereas in London the standard was exceeded at only two (Camden
and Marylebone Road) out of eight sites (Air Quality Consultants Ltd, 2002). Never-
theless, in a more recent official study (DEFRA, 2005), kerbside levels of nanoparticle
emissions were much higher at the Marylebone Road site than background levels at
other sites, both in London and elsewhere. In another study, in Winchester town centre,
emissions failed to achieve the 24-hour mean standard of 50 [ig m~ 3 (as measured by
thirty-five failures per year or more) in five years 1997-2004, and last passed in 2001
(City of Winchester, 2005). A major problem with the UK monitoring hitherto is that it
mainly tests for PM 10 emissions; for example, there are only three PM 2 5 monitoring
sites in London compared with thirty-five PM 10 sites (Air Quality Consultants Ltd,
2003). It is clear now that the dangers from roadside PM are much more serious for
PM 2 5 and ultrafine particles.

A third and obvious qualification is that 90% of PM emissions are not transport
related, so that a comprehensive assessment of their health impacts needs to look at
other sources. This is a complex question, however, because there is not a one-to-one
correspondence between emission sources and health impacts. For example, particulate
emissions are particularly high in agricultural regions and desert areas, both of which
are relatively sparsely populated.

166

C-H C Bae, G Sandlin, A Bassok, S Kim

8 Policy and planning implications

As we have reported, there is an increasing mound of evidence that freeways, major
arterials, and other traffic density zones inflict significant health damage on those
who live and work nearby. Also, recent research shows that the most dangerous
pollutants are ultrafine particles and diesel-related VOCs, whereas the jury is still
out on the long-term health impacts of ground-level ozone, in part because of the
possibility that, within certain ranges, NO x may have protective effects against ground
level ozone exposure. A problem is that scientists disagree about the dollar value of these
health costs, although all of them agree that these costs are very high. There is little effort
to mitigate these freeway-related pollution impacts, although a start is being made in
California (California Environmental Protection Agency and California Air Resource
Board, 2005).

However, without the advantage of a relatively precise cost -benefit analysis, it is
difficult to prescribe the most appropriate policy and planning responses. Because the
scientific community is only now beginning to appreciate the extent of the dangers, it is
unreasonable to expect that people living or working near freeways would have the
same degree of information. They are probably well aware of the noise and somewhat
aware of the higher pollution levels. In making their location decision, residents
trade-off lower rents and house prices against these disadvantages, but with limited
information. So, a 'first, do no harm' principle would be to give the information as it
becomes available to residents to that they can decide (at lease end or some other
stage) whether to move or stay.

Another issue is that the conclusions about air-pollution emissions near freeways
are still largely based on infrequent surveys or inferences from other locations. A key
priority is to set up roadside air-pollution monitors at regular intervals along freeway
routes. This is not a zero-cost option, but the costs are relatively low compared with
the benefits from increased knowledge that can empower the will to act.

Assuming that the knowledge of more damage will be built up over time, what
else (if anything) should be done about it? One obvious option for planners is zoning
remedies. These could range in scope from land-use restrictions on new housing
(planners have allowed 4387 housing units to be built in the Seattle FAPS since
1990, one fifth of the housing built since 1900), schools, daycare centers, senior-
citizen centers, and other facilities, to the compulsory relocation of existing land
uses (with or without compensation). An intermediate position is to allow commer-
cial and industrial facilities, although worker exposure is also a problem (even if
mitigated by reduced hours of exposure relative to residents, lower employment
densities than residential densities and up-to-date filtration systems in modern office
buildings).

A different route is via the strengthening of the controls on the sources — that is,
trucks, cars, and other vehicles. However, even assuming the political will exists,
these policy changes would take a long time (more than twenty years) to implement.
Limitations on new-truck diesel emissions do not begin until 2007, and the truck fleet
is very durable (with an average lifetime of up to thirty years). Restrictions on truck
routes, and perhaps even times of travel, may merit inquiry, but many metropolitan
areas (including Seattle and Portland) have a too-limited highway infrastructure to
permit much route diversion. There are some modest technical fixes: ultra-sulfur
and high-performance diesel fuel and financial incentives to retrofit particle traps, and
retrofitting to use alternative fuels. New automotive technologies (from hybrids to fuel
cells) are beginning to show promise, but rates of adoption and fleet turnovers are
very slow.

Disadvantaged populations in freeway air-pollution sheds

167

Yet another approach is via better health monitoring of and targetted healthcare
for residents and workers within FAPS. This would help, but the sharply rising costs
of healthcare are a major obstacle, given the evidence of the numbers with impaired
health. Also, in any event, prevention is much better than a cure.

Pragmatically, we may have to be content with more modest strategies:

(1) more research and education on air quality (Stone, 2003), finding ways of making
new knowledge accessible so that locators can make more informed decisions.

(2) roadside monitoring of emissions; and

(3) restrictions on new land uses within FAPS.

The latter point brings up an important concern. Infill of all vacant sites and the
redevelopment of obsolete land uses at higher residential densities are very consistent
with the densification objectives embedded in the Growth Management Acts of
Washington and Oregon. However, many of these sites are close to freeways, and
developing or redeveloping them exposes people to significant pollution-related health
damage. Land-use planning and regulations are currently ill prepared to mitigate
these impacts.

9 Conclusions

This research tested three related hypotheses: (1) minority and/or low-income households
live disproportionately close to freeways compared with white and middle-income
households; (2) households in each category cluster together in local subhousing
markets; and (3) negative environmental externalities near freeways (especially air
pollution) are capitalized in house prices and rents. First, the results support all three
hypotheses and their corollaries: the clustering of low-income and minority population
near freeways, and the higher concentration of minority and/or poor students in FAPS.
Health consequences for these children can be more harmful, because of the effects of
pollution on their lung development. Second, the cluster analysis suggests that the
residential choices of the minority and/or low-income population are limited. Third,
locations within a FAPS are negatively associated with housing prices when other
negative environmental factors such as traffic noise are accounted for. Of course, for
people living in such locations, trade-offs may have to be made: cheaper housing versus
higher health risks.

Of course, there are some qualifications to our research. Our hedonic price analysis
is based on house prices, whereas many residents within FAPS are renters. Also,
ideally, we would like a finer grain of detail for our socioeconomic data than the
census-block groups. The most significant point of all is that, though Zhu et al
(2002) have provided us with a useful grounding for deriving the FAPS boundaries,
their study was based on one metropolitan area and hence cannot take account of
location-specific variations (although the boundaries appear to receive some support
from earlier European studies).

At the practical level, in a perfect world we would need a series of air-pollution
monitoring devices along each freeway in all metropolitan areas, given that this is a
health issue of some importance. The United States could learn from the experiences
of the United Kingdom and European Union with monitoring roadside air pollution.
We also need a strategy to mitigate the costs of this problem (possibly, but not
necessarily, to the extent of compulsory relocation of homes and workplaces within
FAPS); whether this would justify relocation assistance is a question for policymakers.
Only when problems of this kind are seriously addressed can we be assured that the
parallel social problem of environmental justice is receiving the public policy attention
that it demands and deserves.

168

C-H C Bae, G Sandlin, A Bassok, S Kim

Acknowledgements. This research has been supported by the University of Washington Royalty
Research Fund (65-5096). Preliminary results were presented at the 2004 Association of Colleges
and Schools of Planning and the 2005 Western Regional Science Association Conferences. The
authors appreciate valuable comments and encouragement from Professors Chris A Nelson (Virginia
Tech University), Peter Flachsbart (University of Hawaii), Brian Stone (University of Wisconsin,
and David Pitfield (Loughborough University, United Kingdom). Also, special thanks are due to
Professors Jane Koening (Environmental Health) and Timothy Larson (Civil Engineering) at the
University of Washington Particulate Matter Center.

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